System and a method for monitoring the physical condition of a herd of livestock

ABSTRACT

In a system and a method for monitoring the physical condition of a herd of livestock, errors between values predicted in accordance with a time-series model and corresponding measured values are used for determining a confidence interval for a prediction for each animal individually the significance of an error between a prediction and a measured value regarding the likelihood that the animal is in heat or suffers from a disease is automatically assessed for each animal individually. There is no need to determine dedicated confidence intervals for different situations and properties. A better fit of the time-series model automatically results in a narrowed confidence interval.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The system shown in FIG. 1 is integrated with a milking stand 1 formilking individual cows one by one. The milking stand further includes amilking device with four suction cups 2-5, to be connected to a cow forwithdrawing milk from that cow. Milk channels 5′-8′ are connected attheir upstream ends to suctions cups 2-and at their downstream ends tothe conductive sensors 9-12. The conductivity sensors 9-12 are part of aconductivity measurement unit 13. In the conductivity measurement unit13, the milk channels 5′-8′ merge downstream from the conductivitysensors 9-12 into a single milk channel 14 passing through a flow meter15 for measuring the milk yield.

2. Description of the Prior Art

In ‘Modelling Daily Milk Yield in Holstein cows Using Time-seriesAnalysis’ by Deluyker et al. in the Journal of Dairy Science,73:539-548, an experimental method for automatically monitoring thephysical condition of a herd of livestock is described which includesthe steps of: measuring a value of a property at regular intervals fromeach individual, identified animal, storing measurement data inaccordance with the measured values of the measured property for eachindividual, identified animal, determining a prediction for a subsequentmeasured value of that property for the respective individual,identified animal from the stored measurement data regarding thatindividual, identified animal, and generating an attention signal inresponse to an error between the value of the measured property and theprediction for that value above a predetermined level.

In this experimental method, the measured property was the milk yield.

For carrying out the measurements an automated cow identification andmilk yield recording system was used.

After the observation period, a time-series model was formulated forpredicting the milk yield of each milking or set of three successivemilkinq with sets of parameters each generally applicable in aparticular period of time during a lactation for either heifers ormultiparous cows.

A disadvantage of this described method is, that it is cumbersome inthat for each cow the appropriate set of parameters has to be selected.This also forms a potential source of errors. Furthermore, it isunlikely that the determined parameters will also apply to herds of cowsof different races or even herds of other animals (e.g. goats), herdskept in other climates or fed with different types of feed.

SUMMARY OF THE INVENTION

It is an object to the invention to provide a reliable system and amethod for automated monitoring the physical condition of a herd ofanimals which is more universally applicable than the model proposed byDeluyker et al.

According to the invention, this object is achieved by providing asystem as described in claim 1 and a method as described in claim 5.

Since, in the method according to the invention and, in operation, inthe system according to the invention, during a lactation, error dataare stored in accordance with predicted values and correspondingmeasured values for each individual, identified animal, and a confidenceinterval for a prediction is determined for each individual, identifiedanimal, and for that same lactation, from the error data characterizingthe distribution of the errors, the method automatically assesses thesignificance of an error between a prediction and a measured value foreach animal individually from data collected during the respectivelactation. The confidence interval can be determined automatically foreach individual measurement and each individual animal, so there is noneed to input different selected confidence intervals for differentperiods of the lactation, for different categories of animals and fordifferent measured properties. Furthermore, the need for separateresearch to obtain such confidence intervals is obviated.

Since the significance of errors is assessed for each animalindividually, any adverse effect of unreliable predictions due to errorsin the choice of the parameters of the time-series model, if applicable,is reduced. For each individual animal and each monitored variable, thewidth of the confidence interval is automatically adjusted on-line tothe empirically found accuracy of fit of the time-series model and canbe signalled separately to indicate the reliability of the predictions.

The measured property can for example be one of the followingproperties: milk yield, milk temperature, milk conductivity, animalactivity and intake of at least one type of feed.

The method according to the invention has a prophylactic and accordinglyproductivity-increasing effect in that it allows an earlier and morereliable identification of individual animals likely to be ill. Firstly,the sooner animals to be checked by a veterinarian can be identified,the better the chances of recovery and the avoidance of adverse effectson the animal are and the better the chances are that further spread ofa contagious disease through the herd can be avoided. Secondly, animalshaving a bad physical condition are accordingly more prone to catchingdiseases or, if already ill, further diseases. The sooner such animalsare identified, the sooner action can be taken to improve the physicalcondition of such animals and to avoid that the identified animalcatches a disease or a further disease

A productivity-increasing effect can also achieved by earlier oestrusdetection, which allows shortening the calving interval.

According to one particular mode of carrying out the method according tothe invention, the error data are used to characterize the mutualdependence between errors in the predictions of the conductivities ofmilk obtained from different teats (quarters if the animals are cows).The data regarding this dependence are subsequently used for assessingthe significance of errors in the prediction of the conductivity of milkobtained from any one of the teats.

According to a further particular mode of carrying out the invention,the error data collected during a lactation are also used to estimatethe parameters of the time-series model underlying the predictions ofthe measured values during that same lactation for each animalindividually. Thus, for each individual animal, the time-series model isautomatically tailored to an optimal fit to the characteristics of thevariations in time of the respective property of that individual animalas the lactation progresses.

Particular features and advantages of the present invention appear fromthe dependent claims and the detailed description set forth below inwhich reference is made to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of a system according to anembodiment of the invention, and

FIG. 2 is a flow chart of a mode of carrying out the method according tothe invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

The system and the method represented by FIGS. 1 and 2 are presently themost preferred modes of carrying out the present invention. Hereinafter,this method and this system are described in the context of monitoring aherd of cows, but in principle, the invention can also be used formonitoring other animals, provided that at least one property of eachindividual animal can be measured at regular intervals.

The system shown in FIG. 1 is integrated with a milking stand 1 formilking individual cows one by one. The milking stand further includes amilking device with four suction cups 2-5, to be connected to a cow forwithdrawing milk from that cow. Milk channels 5′-8′ are connected attheir upstream ends to suction cups 2-5 and at their downstream ends tothe conductivity sensors 9-12. The conductivity sensors 9-12 are part ofa conductivity measurement unit 13. In the conductivity measurement unit13, the milk channels 5′-8′ merge downstream from the conductivitysensors 9-12 into a single milk channel 14 passing through a flow meter15 for measuring the milk yield.

Between the suction cup 5 and the conductivity measurement unit 13, oneof the milk channels 8 passes through a temperature sensor 16 mountedclosely adjacent the suction cup 5 to reduce the influence of theambient temperature on the temperature measurement.

The milking stand is further provided with a feed dispenser 17 foroffering each milked cow a ration of concentrate. As an alternative,feed dispensers may be provided in feeding stations located outside themilking area. The feed dispenser 17 is adapted to dispense feed as it isconsumed by a cow until a predetermined ration has been consumed. Thefeed dispenser 17 further includes a sensor 18 for monitoring thequantity of feed dispensed to each cow. If the full ration has not beenconsumed when a cow leaves the feed dispenser, the weight of the leftover of the respective ration is calculated.

Each cow of the herd is provided with an activity meter (not shown)which registers a value related to or identical to the cow's activitypattern. To read the values from the activity meters, the milking standis provided with an activity meter reader 19 for reading a registeredvalue from the activity meter of each cow which is milked, is to bemilked or has been milked.

Each cow of the herd is further provided with an identity tag (notshown). The milking stand 1 is provided with a cow identificationstructure 20 adapted for reading the identity tag of each cow which ismilked, is to be milked or has been milked. Animal identificationsystems and systems for monitoring the activity of animals arecommercially available and therefore not further described here.

The conductivity measurement unit 13, the flow meter 15, the temperaturesensor 16, the feed sensor 15, the activity meter reader 19 and the cowidentification structure 20 are each connected to a central dataprocessing structure 21 for processing the measured data regarding eachcow. The data processing structure 21 is connected to a display 22. Inaddition to or instead of the display 22, it is also possible to providedevices for generating audible alarms. Such devices can for example beoperated via a dedicated connection to the data processing structure,via a network (e.g. via the telephone network) or be remotelycontrolled.

The skilled person will appreciate that the data processing structure isalso provided with the necessary peripherals. Although presented in theform of parallel connections, the communication structure between thesensors and readers 15, 16, 18, 19, 20 and the data processing structure21 can also be realised in the form of a wired or wirelessbus-structure, in which each station has a distinct address.

The data processing structure 21 is programmed for storing measurementdata in accordance with the measured properties for each individual,identified animal and for determining a prediction for subsequentmeasured values of these properties for the respective individual,identified animal from the stored measurement data regarding therespective individual, identified animal.

Furthermore, the data processing structure 21 is programmed for storingerror data in accordance with errors between predicted values andmeasured values for each individual, identified animal, for determiningdata characterizing the distribution of the prediction errors for eachindividual, identified animal, for determining a confidence interval fora prediction for each individual, identified animal from the datacharacterizing the distribution of the errors in the predictions of themeasured values, and for activating the signalling device 22 to generatea selected attention signal if an error between the value of a measuredproperty and the prediction for that value is outside the confidenceinterval.

In operation, the invention is implemented as set forth below withreference to the flow chart shown in FIG. 2. The algorithm according tothis flow chart is preferably repeated at each milking.

At each milking of each individual cow, the identification tag is readto identify the respective cow as is denoted by step 23.

Then, values of the milk yield, the milk temperature, the conductivityof the milk obtained from each quarter, the registered value of theactivity meter (which may be read with regular intervals upon milkingsor at other moments) and the amount of concentrate feed consumed or leftover are measured as is denoted by step 24. These measurements are takenat each milking and for each individual, identified animal, i.e. atregular intervals. The measured values are read by the data processingstructure 21 as is denoted by step 25.

On the basis of earlier measurements and predictions, or at the firstmilking of a lactation as an initial set of values and parameters,status data which determine the prediction for each next value to bemeasured and characterize the distribution of errors in previouspredictions have been stored in a memory of the data processingstructure 21 for each individual, identified cow. In step 25, thisstatus of the respective identified cow is read by the data processingstructure 21.

From the status data as read, a prediction for the values measured instep 24 is made as is denoted by step 26. Furthermore, on the basis ofthe error data—available in the form of the variance and the covarianceof earlier predictions and measured values—a variance-covariance matrixof the error is determined for the respective sets of predictions andmeasured values as is denoted by step 27. With theseerror-standardization data, the errors between the current predictionsand the measured values can be standardized.

For the feed intake a different approach is preferred; feed left oversmostly equal zero and are sometimes higher. Experimentally obtained datasuggest that successive left overs are independent and that for eachindividual animal there is a distinct probability distribution for thepercentage of the left over of the concentrates ration, which ispreferably defined by:

p ₀ =P(left over=0%),

p ₁ =P(0%<left over<10%),

p ₂ =P(10%<left over<30%),

p ₃ =P(30%<left over<50%),

p ₄ =P(50%<left over<100%).

This distribution can be used to calculate the probability p_(conc) ofthe various levels of concentrate consumption. If the probabilityp_(conc) as low, an attention signal can be generated or at least acontribution to the likelihood of a special condition is found. Thecow-dependent distribution of the probabilities of different ranges ofquantities of left overs is fitted to measured left overs for each cowusing the Kalmnan filter calculating method as will be describedhereinafter.

For the purpose of eliminating structural differences between differentmilkings (e.g. morning and afternoon milkings), calculations arepreferably each time be based on the combined predicted and measuredvalues of two or more successive milkings.

Since standardization data have been determined for the errors of allpredictions, it can be determined whether the errors between thepredictions and the measured values are within single and combinedconfidence intervals of which the width can be determined accurately inaccordance with the desired balance between sensitivity and specificity.If at least one of the measured values is outside the confidenceinterval or if a combination of errors symptomatic for a particularcondition of an animal occurs, in step 28 it is decided that anattention signal must be generated.

Preferably, attention signals based on a single error are generated ifat least one measured value is outside a confidence interval which,after standardization—and assuming the errors for the animal in healthycondition and not in heat have a normal distribution—are outsidepredetermined confidence intervals. For example a “*” mark can be addedto the identification code of a cow if at least one of the measuredvalues is outside a 95% confidence interval, a “**” mark can be added tothe identification code or a cow if at least one of the measured valuesis outside a 99% confidence interval, and a “***” mark can be added tothe identification code of a cow if at least one of the measured valuesis outside a 99.9% confidence interval. Preferably, the values on thebasis of which the attention signal has been generated and the deviationrelative to the predicted value are displayed and/or printed as well.

Preferably, attention signals based on combinations of errors toindicate the likelihood of heat (for example an the form of “h”, “hh”and “hhh”) are generated if activity is rather high and the combinationof activity, yield and temperature falls outside a certain confidenceinterval. Attention signals to indicate the likelihood of mastitis (forexample in the form of “m”, “mm” or “mmm”) are preferably generated ifthe conductivity error is rather high and the combination ofconductivity, yield and temperature falls outside a certain confidenceinterval. An attention signal indicating the likelihood of otherillnesses (for example in the form “i”, “ii” and “iii”) is preferablygenerated if the combined error of yield, temperature and activity fallsoutside certain confidence intervals and the concentrate intake is at alevel having a low probability under normal circumstances.

If it is decided that an attention signal is to be generated, in step29, the display 22 is controlled to display the selected attentionsignal in association with the identification data of the respectivecow.

After an attention signal has been generated, the model on the basis ofwhich the predictions are being made is not reliable anymore for therespective cow, in particular if the attention signal indicates that avalue outside one of the wider confidence intervals has been measured.Therefore, in step 30, the monitoring of a cow on the basis of thecollected data is in principle stopped in response to an attentionsignal regarding the respective animal, or at least in response to anattention signal above a certain confidence level regarding therespective animal.

If it is decided that no attention signal or no attention signal above apredetermined confidence level is to be generated, the status data forthe respective individual cow are updated using two of the followingthree sets of data: the latest measured values, the latest predictionsand the latest errors between the predictions and the correspondingmeasured values. In the flow chart, this is denoted by step 31.

If, after an attention signal has been generated, verification by thefarmer or by a veterinarian reveals that the attention signal wasunjustified, the measured values are preferably replaced by thepredicted values, so the monitoring of the checked animal can becontinued on the basis of the previously collected data and the dataentered instead of the latest set of measured values. Thus, step 30 canbe overruled in the event of a false positive attention signal. Inaddition, impossible measurement results, such as a milk temperature ofmore than 50° C., are preferably automatically ignored and replaced bythe predicted values, while a warning signal indicating that a measuredvalue has been skipped is displayed or printed. Thus, warning signalsindicating the likelihood of malfunction of the measurement structureare obtained as well. Instead of updating on the basis of predictedvalues, it is also possible to skip the step of updating the statusregarding the property for which no usable measurement result isavailable.

Since the milking stand 1 includes a plurality of suction cups 2-5 and aplurality of milk channels 5′-8′, each connected to one of the suctioncups 2-5 and a measurement sensor 9-12 for measuring the conductivity ofmilk passed through the respective milk channel 5′-8′ is provided, theconductivity of milk obtained from each quarter can be measuredindividually. Furthermore, the data structure 21 is programmed forgenerating an attention signal if the error between the predictedconductivity value and the conductivity value measured by any one of themeasurement sensors 9-12 exceeds a threshold value. Thus, an increasedconductivity which typically indicates an increased likelihood ofmastitis, which typically occurs on or two of the quarters at a time,can be indicated with a very high sensitivity and specificity.

Preferably, the threshold value of the error in the prediction of theconductivity of milk from any quarter is positively related to theaverage error of the corresponding predictions of all quarters. If theconductivity of milk obtained from all quarters is higher than predictedit is more likely that the deviations are caused by other factors thanmastitis, since this disease rarely occurs in all quarterssimultaneously, Therefore, a higher sensitivity and specificity can beobtained it the threshold value for any one quarter in response to whichan attention signal is generated is higher in response to measuredconductivities of milk obtained from the other quarters which are higherthan the predicted conductivity as well.

The sensitivity and the specificity of the monitoring method can befurther increased if it is also taken into account to what extent theconductivities of milk obtained from different quarters are mutuallydependent for each individual animal. This is preferably achieved byproviding that the dependence between the conductivity values of minkfrom different quarters is determined for each animal individually fromthe measured conductivity values of that individual animal, and that,for each individual identified animal, the influence of the averageerror on the threshold value is positively related to the dependencebetween the conductivity values of milk from different quartersdetermined for that individual identified animal. Thus, for individualcows showing a more independent behaviour of the conductivity valuesfrom milk obtained from different quarters, the average error in thepredicted conductivity values is of less influence on the thresholdlevel for any one conductivity value than for individual cows of whichthe variations in the conductivity values of milk obtained fromdifferent quarters show a closely related behaviour.

Under normal circumstances, the measured values of the milk yield, theconductivity, the milk temperature and the activity vary gradually intime, i.e. the successive observations of each property are notindependent of one another. Therefore, the predictions are preferablymade using a time-series model assumed to be valid for healthy cows thatare not in heat; unduly great deviations indicate that this assumptionis no longer valid, so the monitoring utilizing the model is inprinciple stopped as described before.

Appropriate time-series models for the different properties can beestablished by plotting experimental data, examining the correlograms ofthe autocorrelations, selecting an appropriate ARIMA and fitting thechosen model.

Furthermore, the parameters of the time-series model are preferablyestimated for each cow individually from errors between values estimatedfor that individual cow and corresponding measured values as therespective lactation progresses. Thus a time-series model is obtained,which automatically adapts itself to the characteristics of therespective cow (for example to more or less erratic variations in themeasured properties) and other circumstances which influence thecharacteristics of the variations of the observations in time. Thus, noexperiments are needed to establish the best parameter settings of thetime-series model under different circumstances, for example to takeinto account the number of days in milk, the race of the cow, theclimate, the feed, the milking habits, different categories of cows(heifers or multiparous cows) etc. Other advantages are, that the riskof inadvertently setting the wrong parameter values is avoided, themethod is generally easier to manage and differences between individualcows and successive lactations of each cow are also taken into account.It is noted that the on-line estimation of the parameters of thetime-series model is also advantageous in that it provides an automatictailoring of the time-series model to each individual animal if theconfidence interval is not individually determined for each individualanimal.

The parameters of the time-series model are preferably estimated using aKalman filter including a state vector determining the prediction forthe next measurement, in which state vector the parameters of thetime-series mode; are included.

The Kalman filter is a method to estimate the state of a system on-line.The state is a quantity that determines the coming behaviour of thesystem. The estimate is improved after each new observation by using thenew information. First, a general description is given and later twoapplications in the method according to the invention are described, inwhich the state comprises (1) the parameters in the time-serles modelsand (2) the probability distribution of the percentage of the calculatedconcentrates left over.

To apply the Kalman filter, the system is described by state-spaceequations in the form of:

an observation equation:

y _(t) =C _(t) x _(t) +v _(t)  (1),

and

a system equation:

x _(t) +A _(t) X _(t−1) +w _(t)  (2).

In these equations x_(t) is the state vector, y_(t) the observationvector, C_(t) and A_(t) are system matrices, v_(t) is the randomobservation error and w_(t) is the random system error. The observationequation describes the relationship between the measurements and thestate, which itself is not directly measurable in general. The systemequation defines the relation between the state at successiveobservations. The distribution of v_(t) is N(0, V_(t)) and thedistribution of w_(t) is N (0, W_(t)).

The estimate of the state x_(t) at observation t is obtained using themeasured values obtained at the observations y₁ to y_(t−1). The Kalmanfilter provides a new estimate of the state after each set ofobservations and furthermore a variance-covariance matrix for the stateestimate.

More in particular, the Kalman filter is a two-stage estimationprocedure. In the first stage, an estimate of the state and thevariance-covariance matrix is calculated on the basis of the previousstate. In the second stage, this estimation is updated in accordancewith the set of observations y_(t) and the estimation error e_(t)(representing the differences between the values obtained during the setof observations and the predictions). The updated estimates are used inconnection with the next set of observations.

The Kalman filter gives the minimium mean square linear estimator ofx_(t). Furthermore, the variance-covariance matrix ot the estimationerror e_(t)—on the basis of which the standardization discussed abovecan be carried out—can also be calculated.

In conventional usage of the Kalman filter in connection with atime-series model, the state would consist of the measured variables.According to the preferred mode of carrying out the invention, theKalman filter is used to estimate the parameters of the time-seriesmodels of the cow variables, therefore the state includes theseparameters. The Kalman filter gives a new estimate of the state aftereach milking, which means new estimates of the parameters of thetime-series models. With these new estimates of the parameters, newmeasurement values are forecasted so that deviant measurements can besignalized reliably and without having to pre-select pararameters of thetime-series model which are believed to provide the best fit in therespective situation. The variance-covariance matrix of the estimatedstate can furthermore be used to relate the errors between predictionsand measured values mutually as described above.

The Kalman filter calculation method can also be used to fit theprobability distribution of the predetermined concentrate consumptionlevels with the concentrate consumption levels. To achieve this, adescription with state-space equations (1) and (2) is used in this casethe following definitions apply: $\begin{matrix}{{x_{t} = \begin{bmatrix}p_{0} \\p_{1} \\p_{2} \\p_{3} \\p_{4}\end{bmatrix}},\quad {y_{t} = \begin{bmatrix}r_{0} \\r_{1} \\r_{2} \\r_{3} \\r_{4}\end{bmatrix}},\quad {A_{t} = I},\quad {C_{t} = I}} & (3)\end{matrix}$

Again, the vector x_(t) defines the state (here the probabilitydistribution) and the vector y_(t) is determined by the set ofobservations with r_(i) defined as follows: $\begin{matrix}{{- \quad \text{if left over}} = {0\%}} & {{r_{0} = 1},\quad {r_{i} = {{0\quad {if}\quad i} \neq 0}}} \\{{{- \quad {if}}\quad 0\%} > \text{left over} > {10\%}} & {{r_{1} = 1},\quad {r_{i} = {{0\quad {if}\quad i} \neq 0}}} \\{{{- \quad {if}}\quad 10\%} > \text{left over} > {30\%}} & {{r_{2} = 1},\quad {r_{i} = {{0\quad {if}\quad i} \neq 2}}} \\{{{- \quad {if}}\quad 30\%} > \text{left over} > {50\%}} & {{r_{3} = 1},\quad {r_{i} = {{0\quad {if}\quad i} \neq 3}}} \\{{{- \quad {if}}\quad 50\%} > \text{left over} > {100\%}} & {{r_{4} = 1},\quad {r_{i} = {{0\quad {if}\quad i} \neq 4.}}}\end{matrix}$

The matrices A_(t) and C_(t) are equal to the identity matrix I,V_(t)=and W _(t)=0.01 I.

With these definitions, the estimation error is: $\begin{matrix}{e_{t} = \begin{bmatrix}{r_{0} - p_{0}} \\{r_{1} - p_{1}} \\{r_{2} - p_{2}} \\{r_{3} - p_{3}} \\{r_{4} - p_{4}}\end{bmatrix}} & (4)\end{matrix}$

A component of e_(t) is positive when r_(i)=1 and negative when r_(i)=0.

For further details regarding the Kalman filter calculation technique,reference is made to ‘Forecasting structural time-series models and theKalman filter’ by A. C. Harvey, Cambridge University Press, CambridgeUK, 1989 and ‘Bayesian forecasting’ by P. J. Harrison & C. F. Stevens,J. of the Royal Stat. Soc., 38, p.205-247, 1976.

The monitoring method and system according to the presently mostpreferred mode of carrying but the invention have been testedexperimentally. Results indicate that already without fine-tuning a verygood sensitivity and a good specificity are obtained as appears from thetables set forth below (the numbers of stars correspond to theconfidence intervals described above):

TABLE 1 Sensitivity and specificity for heat based on 537 cases and41803 milkings outside heat periods. attention sensitivity specificity *94.2% 94.5% ** 86.5% 96.9% *** 82.5% 98.1%

TABLE 2 Sensitivity for disease (mastitis excluded) and specificity ofthe detection model, based on 263 cases and 40286 milkings outsideillness periods. attention sensitivity specificity * 99.6% 86.0% **90.5% 93.5% *** 76.8% 96.7%

TABLE 3 Sensitivity for four different mastitis types and thespecificity for mastitis. sens. sens. sens. sens. clinical subclin.latent secretion mastitis mastitis mastitis disturb. attention (52cases) (21 cases) (35 cases) (36 cases) specificity * 96% 100%  89% 97%95.3% ** 90% 76% 57% 86% 98.2% *** 65% 57% 37% 67% 99.4%

What is claimed is:
 1. A system for monitoring the physical condition ofa herd of livestock comprising: a measurement device for measuring avalue of at least one property associated with an individual, identifiedanimal of the herd, an identification structure for identifyingindividual animals of the herd, a data processing structure operativelyconnected to said measurement device and to said identificationstructure, and a signaling device for generating attention signalsconnected to said data processing structure, said data processingstructure being programmed for: collecting measurement and error data inaccordance with previously measured and predicted values of said atleast one property associated with each individual, identified animal,determining a prediction for at least one subsequent measured value ofsaid at least one property for said individual, identified animal fromsaid stored measurement and error data associated to said individual,identified animal, determining a confidence interval for a predictionfor each individual, identified animal from said error data, measuring avalue of at least one property at regular intervals from eachindividual, identified animal, comparing measured values withcorresponding predicted values and confidence intervals, and activatingthe signaling device to generate an attention signal in response to anerror between the value of said at least one measured property and theprediction for that value outside said confidence interval.
 2. A systemaccording to claim 1, wherein the measurement device comprises aconductivity measurement unit for measuring the conductivity of milkobtained from an individual, identified animal, and said data processingstructure is programmed for generating an attention signal if the errorbetween a predicted value and the conductivity value measured by saidmeasurement unit exceeds a threshold value.
 3. A system according toclaim 2, wherein a milking stand for milking an individual animal, saidmilking stand having a plurality of suction cups and a plurality of milkchannels, connected to the suction cups, respectively, the conductivitymeasurement unit including sensors for measuring the conductivity ofmilk passing through the respective milk channels for individuallymeasuring the conductivity of milk obtained via each suction cup,wherein said data processing structure is programmed for generating anattention signal if the error between the predicted conductivity valueand the conductivity value measured by any one of said measurementsensors exceeds a threshold value.
 4. A system according to claim 1,further including a measurement sensor for measuring the intake of atleast one type of feed by each individual, identified animal, whereinsaid data processing structure is programmed for determining, for eachindividual, identified animal, a time-independent probabilitydistribution from measured feed intakes and for generating an attentionsignal if the measured intake of said type of feed by an individual,identified animal is below a predetermined probability level.
 5. Amethod for automatically monitoring the physical condition of a herd oflivestock including the steps of: collecting measurement and error datain accordance with previously measured and predicted values of at leastone measured property for each individual, identified animal, measuringa value of said at least one property at regular intervals from eachindividual, identified animal, determining at least one prediction foreach individual, identified animal from said error data, comparingmeasured values with corresponding predicted values and confidenceintervals, and generating an attention signal in response to an errorbetween the value of said at least one measured property and theprediction for that value above a predetermined level determined by saidconfidence interval.
 6. A method according to claim 5, wherein themeasured property includes the conductivity of milk obtained from eachindividual, identified animal and an attention signal is generated ifthe error between the predicted conductivity value and the measuredconductivity value for any individual, identified animal exceeds athreshold value individually determined for that individual, identifiedanimal.
 7. A method according to claim 6, wherein said conductivity isindividually measured for milk obtained from each teat and an attentionsignal is generated if the error between the predicted conductivityvalue and the measured conductivity value of milk obtained from any teatexceeds a threshold value individually determined for milk obtained fromthat teat of that individual, identified animal.
 8. A method accordingto claim 7, wherein said threshold value of the error in the predictionof the conductivity of milk from any teat is positively related to theaverage error of the corresponding predictions of all teats.
 9. A methodaccording to claim 8, wherein the dependence between the conductivityvalues of milk from different teats is determined for each animalindividually from the measured conductivity values of that individualanimal, and, for each individual, identified animal, the influence ofthe average error on said threshold value is positively related to thedependence between the conductivity values of milk from different teatsdetermined for that individual, identified animal.
 10. A methodaccording to claim 6, wherein said prediction is made using atime-series model and parameters of the time-series model are estimatedfor each animal individually from errors between values estimated forthat individual animal and corresponding measured values.
 11. A methodaccording to claim 10, wherein said parameters are estimated using aKalman filter including a state vector determining the prediction forthe next measurement, the parameters of the time-series model beingincluded in said state vector.
 12. A method according to claim 6,wherein during each measurement the values of at least two propertiesare measured, the errors in the predictions are standardized using saiderror data, and an attention signal is generated if the combinedstandardized errors are outside a predetermined confidence interval. 13.A method according to claim 6, wherein for each individual, identifiedanimal a time-independent probability distribution is determined frommeasured feed intakes and an attention signal is also generated if themeasured intake of said type of feed by an individual animal is below apredetermined probability level.